A theory of decisional power

The concept of decisional power is introduced and compared with other measures for power like those of Shapley‐Shubik, Banzhaf and Dahl. The sum of the powers of all actors in a social network is maximal for certain structures of the network. These structures are called completely democratic. Completely democratic structures are characterized. In every network dictator sets are present. These can be found by use of Boole polynomials. Decisional power turns out to‐have both a causal aspect and a utility aspect and to allow for variations that express the power over other persons as well as the power in a certain state of the network.     

基于复杂网络理论的含分布式发电的电力网络脆弱度评估

基于复杂网络理论研究含分布式发电(DG, Distributed Generation)的电力网络脆弱度评估问题,有针对性地提出三类脆弱度评估指标,其中基于结构的脆弱度指标能够体现网络拓扑和节点功率对系统供电效率的影响;攻击脆弱度指标可用于评估系统抵御节点和线路移除的能力;基于运行方式的脆弱度指标能够反映整个电网有功功率在传输距离上的均衡度.仿真算例验证了所提指标的有效性和DG对于改善系统功率传输性能与提高抗干扰能力方面的作用.     

An Iterative Exact Solution for the Dual Power Management Problem in Wireless Sensor Network

We study the dual power management problem in wireless sensor networks. Given a wireless sensor network with two possible power levels (heigh and low) for each sensor, the problem consists in minimizing the number of sensors assigned heigh power while ensuring the connectivity of the network. We formulate the problem by a binary integer programming model to minimize the total power consumption. Since the problem is NP-complete, we provide an iterative approximation based on iterative methods in combinatorial optimization. We solve the separation subproblem as a minimum spanning tree.     

Power supply–demand balance in a Smart Grid: An information sharing model for a market mechanism

In the future, global energy balance of a Smart Grid system can be achieved by its agents deciding on their own power demand and production (locally) and the exchange of these decisions. In this paper, we develop a network model that describes how the information of power imbalance of individual agents can be exchanged in the system. Compared to existing network models with hierarchical structures, our developed model, together with a market mechanism, achieve the power balance in the system in a completely distributed way. Additionally, dynamics, constraints and forecasts of each agent can be conveniently involved.     

The power‐of‐pull of economic sectors: A complex network analysis

To compare the relative power of individual sectors to pull the entire economy, i.e., the power‐of‐pull, this article utilizes a complex system perspective to model the economy as a network of economic sectors connected by trade flows. A sector's power‐of‐pull is defined and calculated as a function of the powers‐of‐pull of those sectors that it pulls through network linkages, and their powers‐of‐pull are, in turn, functions of those sectors that they further pull ad infinitum throughout the network. Theoretically, boosting activities in sectors with a higher power‐of‐pull will generate greater network effects while stimulating the entire economy, especially during recessions. This method is applied to the United States in the years before and after the 2008 financial crisis. The results provide a fresh look at the U.S. government's economic revival policies and reveal fundamental changes in the economic structure of the U.S. This work advocates a network‐based analysis of the economy as a complex system. © 2013 Wiley Periodicals, Inc. Complexity 18: 37–47, 2013     

A matrix-based VaR model for risk identification in power supply networks

This paper presents a value-at-risk (VaR) model based on the singular value decomposition (SVD) of a sparsity matrix for voltage risk identification in power supply networks. The matrix-based model provides a more computationally efficient risk assessment method than conventional models such as probability analysis and sensitivity analysis, for example, and provides decision makers in the power supply industry with sufficient information to minimize the risk of network collapse or blackouts. The VaR model is incorporated into a risk identification system (RIS) programmed in the MATLAB environment. The feasibility of the proposed approach is confirmed by performing a series of risk assessment simulations using the standard American Electric Power (AEP) test models (i.e. 14-, 30- and 57-node networks) and a real-world power network (Taiwan power network), respectively. In general, the simulated results confirm the ability of the matrix-based model VaR model to efficient identify risk of power supply networks.     

A quality of service negotiation-based vertical handoff decision scheme in heterogeneous wireless systems

This paper introduces a novel vertical handoff decision scheme. The objective is to provide users with enhanced quality of service (QoS) and maximize the network revenue. This scheme balances both-side interests via a suitably defined network merit function and a user–operator negotiation model. The merit function evaluates network performance based on user preferences and decides the most appropriate network for users. The negotiation model is defined as a semi-Markov decision process (SMDP). An optimal policy that maximizes the network revenue without violating QoS constraints is found by resolving the SMDP problem using Q-learning. Furthermore, a time-adaptive QoS monitoring mechanism is combined with the merit function in order to decrease the power consumption on terminal interface activation. The simulation results demonstrate that the proposed vertical handoff decision scheme enhances the performance in terms of power consumption, handoff call-dropping probability (HCDP) and network revenue.     

Developing concurrent investment plans for power generation and transmission

Decisions on electric power generation and transmission investments may have crucial effects on the development of industrial and residential areas. Decisions made on the infrastructure should have economically beneficial consequences for producers and consumers. The aim of this paper is to propose a model that considers transmission and generation investments simultaneously. The proposed model fills in the gap between models for developing long-term power generation policies and instantaneous power flow models. Unlike other investment models, it explicitly takes the high voltage transmission network into account and the selection of new generation plants located on the interconnected network is made in a more realistic manner considering transmission bottlenecks.The problem subsumes the capacitated network location problem and the network design problem, the former being related to decisions on generation expansion and the latter to decisions on transmission network expansion. The integrated model becomes NP in both feasibility and optimality, because of the sub-problems it contains. Here, a practical procedure is proposed to achieve overall feasibility and also to improve investment decisions when the solution is feasible. The model is tested on the dense interconnected network of an industrialized region in Turkey. The implementation shows how future infeasibilities in the transmission network are highlighted by the model and how generation investment decisions are affected by network expansion alternatives.     

Globally solving a class of optimal power flow problems in radial networks by tree reduction

We devise an algorithm for finding the global optimal solution of the so-called optimal power flow problem for a class of power networks with a tree topology, also called radial networks, for which an efficient and reliable algorithm was not previously known. The algorithm we present is called the tree reduction/expansion method, and is based on an equivalence between the input network and a single-node network. Finally, our numerical experiments demonstrate the reliability and robustness of our algorithm.     

Workforce routing and scheduling for electricity network maintenance with downtime minimization

We investigate a combined routing and scheduling problem for the maintenance of electricity networks. In electricity networks power lines must be regularly maintained to ensure a high quality of service. For safety reasons a power line must be physically disconnected from the network before maintenance work can be performed. After completing maintenance work the power line must be reconnected. Each maintenance job therefore consists of multiple tasks which must be performed at different locations in the network. The goal is to assign each task to a worker and to determine a schedule such that the downtimes of power lines and the travel effort of workers are minimized. For solving this problem, we combine a Large Neighborhood Search meta-heuristic with mathematical programming techniques. The method is evaluated on a large set of test instances which are derived from network data of a German electricity provider.     

Topology design for on-demand dual-path routing in wireless networks

One way to achieve reliability with low-latency is through multi-path routing and transport protocols that build redundant delivery channels (or data paths) to reduce end-to-end packet losses and retransmissions. However, the applicability and effectiveness of such protocols are limited by the topological constraints of the underlying communication infrastructure. Multiple data delivery paths can only be constructed over networks that are capable of supporting multiple paths. In mission-critical wireless networks, the underlying network topology is directly affected by the terrain, location and environmental interferences, however the settings of the wireless radios at each node can be properly configured to compensate for these effects for multi-path support. In this work we investigate optimization models for topology designs that enable end-to-end dual-path support on a distributed wireless sensor network. We consider the case of a fixed sensor network with isotropic antennas, where the control variable for topology management is the transmission power on network nodes. For optimization modeling, the network metrics of relevance are coverage, robustness and power utilization. The optimization models proposed in this work eliminate some of the typical assumptions made in the pertinent network design literature that are too strong in this application context.     

Clustering approach to model order reduction of power networks with distributed controllers

This paper considers the network structure preserving model reduction of power networks with distributed controllers. The studied system and controller are modeled as second-order and first-order ordinary differential equations, which are coupled to a closed-loop model for analyzing the dissimilarities of the power units. By transfer functions, we characterize the behavior of each node (generator or load) in the power network and define a novel notion of dissimilarity between two nodes by the \(\mathcal {H}_{2}\)-norm of the transfer function deviation. Then, the reduction methodology is developed based on separately clustering the generators and loads according to their behavior dissimilarities. The characteristic matrix of the resulting clustering is adopted for the Galerkin projection to derive explicit reduced-order power models and controllers. Finally, we illustrate the proposed method by the IEEE 30-bus system example.     

Dynamic modeling of the power market and analysis of its complex behavior based on a nonlinear complementarity function

In order to accurately simulate the dynamic decision-making behaviors of market participants, a new dynamic model of power markets that considers the constraints of realistic power networks is proposed in this paper. This model is represented by discrete difference equations embedded within the optimization problem of market clearing. Compared with existing dynamic models, the remarkable characteristic of the proposed model is twofold: it accurately reflects the process of market clearing by the Independent System Operator (ISO) while considering the inherent physical characteristics of power networks, i.e., the complex network constraints; and it describes the market condition that the generation and demand sides bid simultaneously. Using a nonlinear complementary function, the complex discrete difference dynamic model is transformed into a set of familiar discrete difference algebraic equations. Then, the complex dynamic behaviors of power markets are quantitatively analyzed. Corresponding to different operating conditions of power network, such as congestion or non-congestion, the Nash equilibrium of power markets and its stability are calculated, and the periodic and even chaotic dynamic behaviors are exhibited when the market parameters are beyond the stability region of the Nash equilibrium.     

An Ergodic Algorithm for the Power-Control Games for CDMA Data Networks

In this paper, we consider power control for the uplink of a direct-sequence code-division multiple-access data network. In the uplink, the purpose of power control is for each user to transmit enough power so that it can achieve the required quality of service without causing unnecessary interference to other users in the system. One method that has been very successful in solving this purpose for power control is the game-theoretic approach. The problem for power control is modified as a Nash equilibrium problem in which each user can choose its transmit power in order to maximize its own utility, and a Nash equilibrium is an ideal solution of the power-control game. We present a noncooperative power-control game in which each user can choose the transmit power in a way that it gets the sufficient signal-to-interference-plus-noise ratio and maximizes its own utility. To ensure the existence of a solution, we also propose the variational inequality problem which is connected with the proposed game. On a linear receiver, we deal with the matched filter receiver. Next we present a new ergodic algorithm for the proposed power control because the existing iterative algorithms can not be applied effectively to the proposed power control. We also present convergence analysis for the proposed algorithm. In addition, applying the proposed algorithm to the proposed power control, we provide numerical examples for the transmit power, the signal-to-interference-plus-noise ratio and so on. Numerical results for the proposed algorithm shall show that as compared with the existing power-control game and its method, all users in the network can enjoy the sufficient signal-to-interference-plus-noise ratio and achieve the required quality of service.      

BP神经网络在农机总动力预测中的应用

准确的预测黑龙江省农机总动力,可为黑龙江省的农业机械化发展趋势和农机产品市场分析提供理论指导,为制定黑龙江省农业机械化发展规划和预测近阶段农业机械化发展水平提供参考依据.利用黑龙江省1980-2007年农机总动力数据,运用标准BP神经网络和改进BP神经网络模型进行预测,预测结果表明,改进BP神经网络模型比标准BP神经网络模型在预测精度、运行时间、学习次数等方面更具优越性.     

Probabilistic Analysis of Failures in Power Transmission Networks and Phase Transitions: Study Case of a High-Voltage Power Transmission Network

We present the failure analysis of a study case of a high-voltage power transmission network using the mathematical model of cascading blackouts introduced in Carreras et al. (Chaos 12:985–994, 2002). When the load of the network is randomly perturbed, we study the probability density function of the measure of the size of the resulting blackout as a function of the mean load level. The mathematical model used approximates the network with an undirected graph made of generator, load and junction nodes connected by branches representing the lines of the network. The electric flow in the network is found solving the optimal DC power-flow problem and the sequence of events causing a cascading blackout is simulated using a numerical scheme. The analysis points out the existence of values of the mean total power demand such that for higher values when the blackout size measure increases the decay of the blackout size measure probability density function changes from being best fitted by a negative exponential to being best fitted by an inverse power law. The analogies between this phenomenon and the phase transition phenomenon studied in statistical mechanics are discussed. The website: contains some auxiliary material including animations that helps the understanding of this paper. The numerical experience reported in this paper has been obtained using the computing grid of ENEA (Roma, Italy).     

Autonomous localization based energy saving mechanism in indoor MANETs using ACO

The biggest challenge in MANETs is to find most efficient routing due to the changing topology and energy constrained battery operated computing devices. It has been found that Ant Colony Optimization (ACO) is a special kind of optimization technique having characterization of Swarm Intelligence (SI) which is highly suitable for finding the adaptive routing for such a type of volatile network. The proposed ACO routing algorithm uses position information and energy parameters as a routing metric to improve the performance and lifetime of network. Typical routing protocols have fixed transmission power irrespective of the distance between the nodes. Considering limiting factors, like small size, limited computational power and energy source, the proposed solution excludes use of GPS for identifying the distance between nodes for indoor MANETs. The distance between nodes can be determined by using Received Signal Strength Indicator (RSSI) measurements. Thus, an intelligent ACO routing algorithm with location information and energy metric is developed to adaptively adjust the transmission power and distribute the load to avoid critical nodes. Proposed Autonomous Localization based Eligible Energetic Path_with_Ant Colony Optimization (ALEEP_with_ACO) algorithm ensures that nodes in the network are not drained out of the energy beyond their threshold, as the load is shared with other nodes, when the energy of a node in the shortest path has reached its threshold. Hence, the total energy expenditure is reduced, thus prolonging the lifetime of network devices and the network. We simulated our proposal and compared it with the classical approach of AODV and other biological routing approaches. The results achieved show that ALEEP_with_ACO presents the best Packet Delivery Ratio (PDR), throughput and less packet drop specially under high mobility scenarios.     

PowerNet: Efficient Representations of Polynomials and Smooth Functions by Deep Neural Networks with Rectified Power Units

Deep neural network with rectified linear units (ReLU) is getting more and more popular recently. However, the derivatives of the function represented by a ReLU network are not continuous, which limit the usage of ReLU network to situations only when smoothness is not required. In this paper, we construct deep neural networks with rectified power units (RePU), which can give better approximations for smooth functions. Optimal algorithms are proposed to explicitly build neural networks with sparsely connected RePUs, which we call PowerNets, to represent polynomials with no approximation error. For general smooth functions, we first project the function to their polynomial approximations, then use the proposed algorithms to construct corresponding PowerNets. Thus, the error of best polynomial approximation provides an upper bound of the best RePU network approximation error. For smooth functions in higher dimensional Sobolev spaces, we use fast spectral transforms for tensor-product grid and sparse grid discretization to get polynomial approximations. Our constructive algorithms show clearly a close connection between spectral methods and deep neural networks: PowerNets with $n$ hidden layers can exactly represent polynomials up to degree $s^n$, where $s$ is the power of RePUs. The proposed PowerNets have potential applications in the situations where high-accuracy is desired or smoothness is required.     

云计算在智能电网中的应用研究

随着现代电网规模的不断扩大,现有的电力系统网络越来越无法满足现代化建设发展的需求,具备自愈、互动、兼容等优点的智能电网成为未来电网的发展趋势.同时,智能电网概念的提出和逐步实现对电力系统的计算和存储能力提出了越来越高的要求.基于云计算技术并结合电力网络系统的特点,提出电力网络系统智能云的概念,在此基础上规划设计了电力系统智能云的体系结构并分析了电力系统智能云实现的关键技术.电力网络系统智能云技术的应用,能够在现有基础资源基本不变的情况下,大大提高电力网络系统的计算和存储能力,实现电力网络系统智能化、信息化和分级化的互动管理,减少电网建设投资,为构建现代化智能电网系统提供了明确的思路和研究方向.